This is a personal story about our real-world experience, which contains little resemblance to most of what is written about entrepreneurism and technology commercialization. While our journey has been longer than most, scientific commercialization (aka deep tech) typically requires two decades or more from theory to market. Even more rare in our case is that the R&D journey has been self-funded and very lean. Although my route was different, my peers in R&D have been scientists in a handful of labs—primarily universities, a few corporations, non-profit institutes and national labs, this model has allowed us to develop one of very few unified AI systems in pure native form free from institutional and other conflicts that too-often kill or ruin much-needed technology and companies based on them.

I’ll begin in the Puget Sound area where my wife Betsy and I met in 1980 while working at Mt. Rainier. A couple years later we started a traditional business. After selling our business Betsy went into banking and I started a consulting firm that worked with a variety of different clients across the Pacific Northwest. We moved to Arizona in 1992 in part due to consulting work cleaning up the S&L crisis for private owners. In 1995 we decided to test the emerging web with a self-guided management system that was distributed in hard copy. That effort became one of the leading networks for small business. It was a first so we experimented with all models. The venture grew rapidly in organic form but needed significant growth capital to reach sustainable maturity. Unlike San Francisco and the Seattle area where nearly every good scalable business was funded, flyover states had little infrastructure to support scalable businesses, even when risk had been mitigated in sustainable form, so we sold prematurely.

The lean KS lab

Kyield’s first office in AZ

The experience with our first online venture was so intense with such profound implications that I converted our consulting firm to one of the original lean venture labs. I retrained in computer programming and built a lab and data center in the building above on our property in Northern Arizona. Our specialty was in knowledge systems (KS — arm of artificial intelligence / AI). Stanford has a well-known KS lab—one of few at the time. Although hundreds of billions USD were wasted on me-too dotcoms in the 1990s, AI was still in an ice age (aka AI winter).

Two decades ago this year I was working in the lab operating a learning network I designed called GWIN (Global Web Interactive Network), which was the most advanced of several experiments we developed from scratch. Primitive by today’s standards, GWIN was a cutting edge network at the time that attracted an impressive membership of leaders in science, business, NGOs and government. Tech CEOs and VCs were among our closest followers, though we had entire boards of in the Fortune 500, intelligence agencies, and hundreds each of professors, investment houses, analysts, NGOs, and editors. Log activity from Air Force One was not uncommon. A nun reporting from the Amazon jungle was one our most interesting members.

The most promising program in GWIN was ‘Lookout’, which was a primitive early digital assistant that delivered personalized news clips sourced from the web containing brief human analysis accompanied by discussion. Although we offered web discussion and chat, email lists were preferred at the time.

GWIN was a fascinating experience that was also producing enormous value. One of many examples was a network-wide warning on hurricane Mitch—second most deadly Atlantic hurricane. A life-long weather geek, I typically had a monitor running radar and sat loops so I watched as Mitch grew into a dangerous slow moving cat 5 heading right for high risk areas, so I issued a warning. Between a few members in Central America, media, corporate and government members with operations in the region, our warning on Mitch spread rapidly. I can still recall the satisfaction in receiving messages from members conveying that by distributing a few lines of text in GWIN we helped save lives and prevent unnecessary losses. Prevention has been one of my personal passions all along. When planned and executed well, prevention can provide the highest possible ROI—in dollars and lives.

Most of the GWIN members didn’t realize that although a team of remote developers helped build the network, I was operating and improving it solo 24/7/365 from my office in our onsite data center. My wife Betsy and I were paying for almost all of the efforts personally other than a small investment at the time from my late partner and friend Dr. Russell E. Borland. By that time a tsunami of capital had arrived in Silicon Valley and Wall Street causing the infamous dotcom bubble, resulting in enormous levels of predation, subsidies, and losses, much of which I considered fraud. Few would pay for online services because of it. It was the largest consumer price war in history so we focused our efforts on deep tech and business rather than consumer.

A new theorem

A few months after the launch of GWIN I received a life-changing call from my brother Brett telling me that he had been diagnosed with ALS. I then dedicated as much time as possible attempting to find promising therapies or tools that could accelerate R&D. Tragically, I discovered that we were a long way from even understanding ALS or obtaining technology that could significantly accelerate effective therapies. Brett passed away three years later within a few days of the estimate by doctors at Mayo Clinic in Scottsdale who confirmed his diagnosis. My quest to find, test and develop more intelligent tools led to a new theorem ‘yield management of knowledge’, which was then followed by piecing together components of a unified AI system in our Kyield OS.

The pathway to the theorem began with a classic aha moment after an extended period of intense work on information overload in operations and research, including testing promising search engines and other methods as they became published. I’m still refining the equation, but it essentially details key factors in optimizing the knowledge yield curve given the needs and constraints of each entity. Although the human brain is amazingly powerful, it does have finite limits beyond which it begins to malfunction, which I first discovered at 30-something in the lab. We were clearly faced with a highly complex systemic problem requiring a systemic solution with the capacity to effectively manage the complexities involved. To help clarify I posed the following question:

If a hypothetical perfect Chief Knowledge Officer (CKO) existed, how would we optimally achieve his/her mission in a network environment, how would it be designed, and what essential components would be required?

That question eventually led to our CKO Engine, which provides governance and security for the entire distributed network. Administration in the Kyield OS is through a simple natural language interface with multiple security levels and methods, some of which are kept secret for security.

It was discovered that multiple obstacles could best be overcome within a single holistic architecture; and without which none of the problems can be fully overcome:

If we do not resolve the problem of information overload, then creativity and productivity suffer.

If we do not resolve the problem of ownership of original work, then innovation suffers.

If we do not provide accurate metrics, then meritocracy cannot function properly.

If we do not provide adaptability, then differentiation and continual improvement will be very difficult to achieve.

If we do not embed intelligence into the files, the most relevant search queries cannot be returned even by the most improved algorithms, thus negatively impacting productivity and innovation.[i]

We realized that it would be at least a decade before essential components matured sufficiently to begin to effectively manage knowledge yield over computer networks. A continuation of Moore’s Law in semiconductors in combination with rapid improvements in bandwidth and algorithmics would be required over an extended period before the theorem could be fully realized in applied form as intended. However, I was confident it would be achievable in my lifetime, even if imperfect.

We were able to test components of the standard system and verify supercomputing results of similar scale and data structure in early 2000s, but scale challenges and bandwidth bottlenecks prevented the ability to deliver functionality to individuals and devices. By the mid-2000s Kyield had matured into a distributed operating system (hence Kyield OS) and essential pieces of the puzzle began to coalesce, so I submitted my AI systems patent application “Modular system for optimizing knowledge yield in the digital workplace.” The 2006 application was granted in 2011 representing about 25% of the total IP/IC at the time.[ii] I viewed the patent as additional insurance.

Initium Capital

Kyield Founder Mark Montgomery at his AZ desk in 2007

In late 2007 I met with Craig Barrett at his office in Chandler Arizona. Although Craig and I were both active with local universities and tech groups in Arizona we had never met, so a mutual friend Les Vadasz introduced us. I won’t go into detail on what we discussed in our one-on-one meeting other than to say it was open, honest, and friendly. Craig may have been approaching mandatory retirement age but he was impressive, helpful, and obviously still at peak performance. A few years earlier I had spearheaded a VC firm (Initium). It had proven suicidal to build high cap ventures in flyover states that depend on capital centers for growth funding. In addition to rare private efforts like our small lab, universities, federal and state governments were investing enormous sums in R&D just to see ventures copied or cherry picked primarily by California (more recently China). In New Mexico most of the spinout ventures from national labs were exported, perpetuating a long-term trend in one of the worst state economies. I warned often of an economic balkanization underway. Few seemed to understand that if that wasn’t fixed most other problems would be trivial.

Our efforts to build Initium hit a similar capital ceiling as individual ventures in the form of lack of regional support. We had one of the strongest teams ever assembled in a flyover state with an unusually large inaugural fund target of $250 million. The fund structure contained a flexible 40% dedicated to the region and 60% with no geographic restrictions. While we earned a place on the emerging leader radar, history had painfully demonstrated the need for key local support and investment. To the extent such regional investment existed it was rare, too risk averse for deep tech and/or unqualified. So we reluctantly sized down Initium and explored merger interest from Bay area firms. Betsy and I liquidated everything but our property and relocated to Half Moon Bay during the first week of 2008, just in time for the financial crisis.

We enjoyed many aspects of living in the Bay area, not least living a block from the ocean after 15 years in the desert, though we found the economic situation troubling. Home prices were several times the cost of where we lived in Arizona and all other costs were much higher as well. It was quite clear why VC investment was so high in SV, contributing to sharply increasing failure rates. The number of homeless served as a constant reminder of just how out of whack the local economy was. Betsy took her first year off work to pursue a hobby in art and wound up working for non-profits as a volunteer attempting to fill some of the massive unmet social needs.

We had a one-year window during which time the financial crisis became increasingly worse and the future of the other firms and investors increasingly uncertain. We were also in discussions with market leaders for OEM-type relationships, but they were clearly not yet prepared for AI systems or Kyield. So after the most costly year of our financial lives other than not investing in pre IPO Microsoft or pre investment in Google (among others), we walked away from a merger that teed up a significant investment in Kyield. Hindsight suggests that our instincts were functioning well as Kyield and the markets were still premature a few years later. It’s unlikely that Kyield would have survived in the SV VC model at the time. Machine learning really took off in 2015 with investment in the tech stack that improved performance and value for majority of use case scenarios.

The city different in the land of (serendipitous) enchantment

Upon arrival at our property back in Arizona in early 2009 we discovered that the caretakers had trashed our property, so we took another financial hit and turned it over to a management company. We then decided to go on a road trip to find a rational place to ride out the financial crisis while maturing Kyield R&D. The plan was to do a loop starting in Tucson, then through New Mexico (NM) to Colorado, perhaps Wyoming and Montana and back through Utah to Arizona. My expectation was to lease a place in Colorado, but fate intervened in the form of a car pulling a u-turn right in front of us outside of Albuquerque on the way to meet a realtor for a house showing. The ensuing collision almost totaled both cars but no one was injured and the driver was very nice as were the police. We were on a schedule, however, so had to rent a car and move on to Santa Fe where the first house we looked at seemed perfect for us and our dogs, so we took it.

We have history in Santa Fe dating back to our first trip in 1985 and also an informal relationship with the Santa Fe Institute (SFI) from our GWIN days that share many others. I also had some interaction with national labs due to Initium. We performed consulting work in NM that included market audits in the 1990s and also covered in VC, so I was familiar with the strengths and weaknesses. One of the world’s leading research centers—more so than most realize, NM is also famously difficult for growing scalable businesses of the type that occasionally emerge from that investment. Despite hundreds of billions of dollars invested in research within NM and large numbers of spinouts, the state has never produced a significant business success in tech. Suffice to say that accidents normally occur with far more frequency.

Terrace at the Santa Fe Institute

I spent quite a bit of time at SFI over the next several years meeting with leading scientists from around the world working on similarly challenging problems in physics, computer science, biology, economics and sociology, which helped indirectly in ways difficult to capture or fully understand. SFI is unique in the world in many respects.

In early 2012 we began presenting Kyield to management in the few organizations that had a supercomputer, sufficient budget and the internal talent to even consider Kyield in organic fashion at the time. Significant progress has since allowed us to steadily expand our focus to mid-market and government markets. When the managed services model is completed as originally intended most markets should be viable.

Byproducts of the voyage (not including R&D pipeline)

IoE (Internet of Entities)

Since the early days of our R&D I have looked at networks as being comprised of entites, not things. The reasons should be self-evident—to the degree they aren’t speaks to the influence on structural issues in the network economy we are working to resolve, some of which are causing serious economic and social damage—namely the business models applied to the web.

Our old colleagues who designed the Internet are the first to admit that it was never designed for many of the tasks required of it today, including commerce or security. Public networks involve many different legal entities, including individual humans and organizations, each of which has unique needs and legal rights. The data carried over networks represents those rights (or should). Even sensors on the network are owned and governed by entities, and they are rapidly becoming more intelligent, hence the need to view networks as entities that contain appropriately engineered governance structure to manage relationships between entities.

Today we offer a suite of IoE options built upon the Kyield OS to manage an enterprise network easily extendable to partners, customers and things (sensors). This is the wisest path from my perspective for managing networks in government, industries, homes, autos, ships, planes, etc. The Kyield OS offers critical elements for optimizing intelligent networks.

The standard Kyield OS

CALO (Continuously Adaptive Learning Organization) is the manifestation of the original modular system invention as applied with state-of-the-art components and algorithms. Recent improvements in machine learning combined with more sophisticated statistical processes and algorithmics within the distributed Kyield OS enable customer organizations to achieve a CALO. The Kyield OS operates substantially in the background with semi-automated controls for each organization, group and individual. Unlike earlier management concepts, CALO is executable.

Health Management Platform

Kyield Health Managment

First unveiled in our diabetes use case scenario paper in 2010 still in futuristic form, which has since been downloaded in the seven figures, the U.S. has yet to deal with the healthcare fiscal time bomb. The sector has evolved over decades to build resistance to efficiencies, cost management and/or patient-centric systems, resulting in the highest cost healthcare system in the world, which provides less quality than others at half the cost. Little progress can be made in U.S. healthcare until reformed by Congress, without which we are limited primarily to the self-insured in the U.S.

HumCat (Prevention of Human Caused Catastrophes)

After many years of focused R&D we announced our HumCat program powered by the Kyield OS. The HumCat program pioneers new territory at the confluence of distributed AI systems, risk mitigation and prevention. By bundling more powerful computing and algorithmics in the Kyield OS with financial incentives and risk transfer through bonds, reinsurance, and other vehicles, we can significantly improve the risk profiles of individuals and organizations and thus lower costs.

It is now possible to prevent many if not most human-caused crises, including accidents, fraud and/or malintent, whether in physical or cyber form. While each organization has unique characteristics requiring bespoke structuring, it is possible to offer select clients limited upfront guarantees that finance and cover the cost of the entire program over a defined period (1-5 years). Higher risk organizations can likely reduce costs significantly and may be able to improve ratings over time as reduced risk is demonstrated with more accurate analytics offered by the Kyield OS. As interest rates rise ratings will become even more critical for corporations and governments.

The HumCat program targets the highest possible ROI events while bundling the individual functions in the Kyield OS such as enhanced security and productivity, representing a significant breakthrough in value to clients and society. We have a great deal of interest in the HumCat program for what are hopefully obvious reasons.

Knowledge Currency

A byproduct of the architecture necessary to execute functionality within the Kyield OS is deep intelligence on workflow and work products from each entity. While Kyield makes no claim on the data ownership or control beyond required by law and as pre-agreed with customers for specific needs, that intelligence does allow us to create and manage an exceptionally valuable digital currency, or knowledge currency. The creation and offering of Kyield’s knowledge currency (KYC) opens up many positive benefits for and between customers, including more accurate valuation of individual, team, and corporate knowledge capital, the ability to be compensated fairly for knowledge work, and the ability to transact and trade intellectual work products in a more rational and accurate manner. In addition to knowledge products created, KYC can be used to value and transact knowledge about an entity, such as health information. At large scale the KYC could have profound economic benefits by substantially overcoming the serious problems across our society caused by the dominant web ad model. KYC has been in our R&D pipeline since the early 2000s.

Where is Kyield today?

We are in discussions and negotiations on various options to build out and scale the Kyield OS in the hybrid managed services model as originally intended. While the system can be installed on top of the infrastructure of others such as AWS, Azure, Google, IBM, and Oracle, we have some proprietary technology that must be installed on our own hardware for an optimal unified Kyield OS. The hybrid configuration typically includes an installed custom computer within the client data center, private cloud or a multi-cloud scenario. This allows us to offer the pre-engineered Kyield OS and additional products while protecting our security as well as customers, reduce unnecessary and costly integration costs, and reduce or eliminate redundancies.

Learn about the background of Kyield and the multi-disciplinary science involved with AI systems, with a particular focus on AI augmentation for knowledge work and how to achieve a continuously adaptive learning organization (CALO).

TABLE OF CONTENTS

INTRODUCTION ……………………………………………………………………………………..

REVOLUTION IN IT-ENABLED COMPETITIVENESS …………………………………………..

POWER OF TRANSDISCIPLINARY CONVERGENCE …………………………………………..

MANAGEMENT CONSULTING ……………………………………………………………………

COMPUTER SCIENCE AND PHYSICS…………………………………………………………….

ECONOMICS AND PSYCHOLOGY ………………………………………………………………..

LIFE SCIENCES AND HEALTHCARE……………………………………………………………

PRODUCTS AND INDUSTRY PLATFORMS…………………………………………………….

KYIELD OS …………………………………………………………………………………………..

THE KYIELD PERSONALIZED HEALTHCARE PLATFORM ………………………………….

ACCELERATED R&D: THE LIVING ONTOLOGY ………………………………………………

SPECIFIC LIFE SCIENCE AND HEALTHCARE USE CASES …………………………………

BANKING AND FINANCIAL SERVICES ………………………………………………………..

THE PILOT PROCESS ……………………………………………………………………………..

EXAMPLE: BANKING, PHASE 1…………………………………………………………………

PHASE 2…………………………………………………………………………………………….

PHASE 3…………………………………………………………………………………………….

PHASE 4…………………………………………………………………………………………….

CONCLUSION: IN THIS CASE THE END JUSTIFIES THE MEANS …………………………21

Visit our learning center to download this ebook and view other publications from Kyield at the confluence of AI systems, crisis prevention, risk management, security, productivity and organizational management.

Ascension to a Higher Level of Performance

The Kyield OS: A Unified AI System

By Mark Montgomery
Founder & CEO
Kyield

I just completed an extensive e-book for customers and prospective customers, which should be of interest to all senior management teams in all sectors as the content impacts every aspect of individual and corporate performance.

Our goals in this e-book are fivefold:

Provide a condensed story on Kyield and the voyage required to reach this stage.

Demonstrate how the Kyield OS assimilates disparate disciplines in a unified manner to rapidly improve organizations and then achieve continuous improvement.

Discuss how advances in software, hardware and algorithmics are incorporated in our patented AI system design to accelerate strategic performance and remain competitive.

Detail how a carefully choreographed multi-phase pilot of the Kyield OS can provide the opportunity for an enduring competitive advantage by establishing a continuously adaptive learning organization (CALO).

Educate existing and prospective customers on the Kyield OS as much as possible without disclosing unrecoverable intellectual capital, future patents and trade secrets.

TABLE OF CONTENTS

INTRODUCTION

1

REVOLUTION IN IT-ENABLED COMPETITIVENESS

2

POWER OF TRANSDISCIPLINARY CONVERGENCE

3

MANAGEMENT CONSULTING

4

COMPUTER SCIENCE AND PHYSICS

5

ECONOMICS AND PSYCHOLOGY

9

LIFE SCIENCE AND HEALTHCARE

10

PRODUCTS AND INDUSTRY PLATFORMS

11

THE KYIELD OS

11

THE KYIELD PERSONALIZED HEALTHCARE PLATFORM

12

ACCELERATED R&D

13

SPECIFIC LIFE SCIENCE AND HEALTHCARE USE CASES

13

BANKING AND FINANCIAL SERVICES

14

THE PILOT PROCESS

15

EXAMPLE: BANKING, PHASE 1

17

PHASE 2

18

PHASE 3

18

PHASE 4

18

CONCLUSION: IN THIS CASE THE END JUSTIFIES THE MEANS

21

To request a copy of this e-book please email me at markm@kyield.com from your corporate email account with job title and affiliation.

I just completed an in-depth paper on how our work and system can help life science and healthcare companies overcome the great challenges they face, so I wanted to share some thoughts while still fresh. The paper is part of our long-term commitment to healthcare and life sciences, requiring a deep dive over the past several weeks to update myself on the latest research in behavioral psychology, machine learning, deep learning, genetics, chemicals, diagnostics, economics, and particle physics, among others. The review included several hundred papers as well as a few dozen reports.

The good news is that the science is improving rapidly. An important catalyst to accelerated learning over the past 20 years has been embracing the multi-disciplinary approach, which academia resisted for many years despite the obvious benefits, but is now finally mainstream with positive impact everywhere one looks.

The bad news is that the economics of U.S. healthcare has not noticeably improved. For a considerable portion of the population it has deteriorated. The economic trajectory for the country is frankly grim unless we transform the entire healthcare ecosystem.

A common obstacle to vast improvement in healthcare outcomes that transcends all disciplines with enormous economic consequences is data management and analytics, or perhaps more accurately; the lack thereof. There is no doubt that unified networks must play a lead role in the transformation of healthcare. A few clips from the paper:

“By structural we mean the physics of data, including latency, entropy, compression, and security methodology. The Kyield system is intended to define structural integrity in NNs, continually exploring and working to improve upon state-of-the-art techniques.”

“While significant progress has been made with independent standards towards a more sustainable network economy, functionality varies considerably by technology, industry, and geography, with variety of data types and models remaining among the greatest obstacles to discovery, cost efficiency, performance, security, and personalization.”

Life science and healthcare are particularly impacted by heterogeneous data, which is one reason why networked healthcare is primitive, expensive, slow, and alarmingly prone to error.

“Biodiversity presents a unique challenge for data analytics due to its ambiguity, diversity, and specialized language, which then must be integrated with healthcare and data standards as well as a variety of proprietary vendor technology in database management systems, logistics, networking, productivity, and analytics programs.”

“Due to the complexity across LS and healthcare in data types, standards, scale, and regulatory requirements, a functional unified network OS requires specific combinations of the most advanced technology and methods available.”

Among the most difficult challenges facing management in mature life science companies are cultures that have been substantially insulated from economic reality for decades, only recently feeling the brunt of unsustainable economic modeling throughout the ecosystem, typically in the form of restructures, layoffs, and in some cases closure. This uncertainty particularly impacts individuals who are accustomed to career security and relatively high levels of compensation. I observed this often during a decade of consulting. The pain caused by a dysfunctional economic system is similar to the diseases professionals spend their careers fighting; often unjustly targeting individuals in a seemingly random manner, which of course has consequences.

“Among many changes for knowledge workers associated with the digital revolution and macro economics are less security, more free agency, more frequent job changes, much higher levels of global venture funding, less loyalty to corporate brands and mature industry models, and considerably increased motivation and activism towards personal passionate causes.”

Healthcare is a topic where I have personal passion as it cuts to the core of the most important issues to me, including family, friends, colleagues, and economics, which unfortunately in U.S. healthcare represents a highly self-destructive model. My brother was diagnosed with Lou Gehrig’s disease (amyotrophic lateral sclerosis/ALS) in 1997 not long after his only child was born. I’ll never forget that phone call with him or what he and his family endured over the next three years even though his case was a fine example of dedicated people and community. My father passed a decade later after a brutal battle with type 2 diabetes; we had an old friend pass from MS recently, and multiple cancers as well as epilepsy are ongoing within our small group of family and friends. So it would be foolhardy to deny the personal impact and interest. Healthcare affects us all whether we realize it or not, and increasingly, future generations are paying for the current generation’s unwillingness to achieve a sustainable trajectory. Unacceptable doesn’t quite capture the severity of this systemic failure we all own a part of.

The challenge as I see it is to channel our energy in a positive manner to transform the healthcare system with a laser focus on improved health and economic outcomes. This of course requires a focus on prevention, reduced complexity throughout the ecosystem, accelerated science, much improved technology, and last but not least; rational economic modeling to included increased competition. The latter will obviously require entirely new distribution systems and business models more aligned with current science and economic environment. Any significant progress must include highly evolved legislation reflecting far more empowerment of patients and dramatic improvement in fiscal discipline for the ultimate payer we call America while there is still time to manage the disease. If we continue to treat only the symptoms of healthcare in America it may well destroy the quality of life for the patient, if indeed the patient as we know it survives at all. This essentially represents my diagnosis.

A few of the 80 references I cited in the paper linked below are good sources to learn more:

First, I want to apologize for not being able to keep up with my blog as much as I would like, or to share as much in public as I would prefer. The reasons are twofold. We’ve been very busy at Kyield, and testing has increasingly confirmed that while competitors in our industry invest heavily in web information (CI), most customers do not; at least for enterprise-wide systems like Kyield. So I have regrettably pulled back on detailed public writing, or rather– have replaced with more formal papers and presentations with customers.

A good example of our efforts is the new report below, which is a hybrid of an academic paper with citations supporting our claims and a detailed brochure for senior managers in pharmaceuticals, biotech, and healthcare–particularly those pursuing personalized medicine and significant improvement in operational efficiency:

The paper highlights the challenges facing the industry with considerable detail on how Kyield is unique in the world with respect to ability to overcome these challenges. Essentially, in order to overcome systemic challenges it requires a systemic solution, and in terms of distributed organizations it requires a very particular type of systemic solution that can address each of the challenges. Due to the high values involved, the result is that Kyield may well be the best investment option in the world today for life science executives.

For those who would prefer more frequent updates, the best methods to track either Kyield or my activity are as follows:

If the financial crisis confirmed anything, it is that the majority of humans are followers, not leaders, and that leaders throughout our society have yet to capture the significance of technology to their members and organizations.

One of the primary causal factors cited by thought leaders in studying crises is poor leadership, to include those who accept misaligned or conflicted interests. When we see “skimming off the top” in others we label it corruption, yet few see it in themselves at all, or choose to ignore it, resulting in the same outcome. While balance is obviously needed for survival—indeed managing that balance well is key for modern leaders, when we over-emphasize short-term profits, we then elevate the influence and power of those who are skilled at winning very short-term battles, rather than long-term wars. I have personally experienced that strategy in organizations and observed it in many others; it doesn’t end well.

One problem with the short-term leadership model is that the skills for software programming, instant trading, manipulating markets, or otherwise amassing great wealth quickly, does not necessarily translate to good leadership in a private company, government, or stewardship in philanthropy. Indeed, in my own observations and those of many others, quite the opposite is often true, yet our information institutions instruct society to emulate the clever rather than the wise. Should we be surprised then at the trend line of manipulation, polarization, and ever deeper crises?

Unlike the early days of the industrial revolution, in the second inning of the information revolution we now understand that most of the challenges facing the human species are long-term in nature, so we must realign our thinking and structures accordingly, including financial incentives and leadership development. Alas, since the long-term has been greatly compressed by consistent failure of short-term behavior, our entire species must now learn to act in the short-term on behalf of our mutual long-term interests. Easier said than done in our culture. The good news is that it’s quite possible…tick-tock, tick-tock, tick-tock.

The process of identifying, mentoring, and recruiting strong leaders often consists of conflicting advice that tends towards self-propelling cultures, regardless of organizational type. In addition to skill sets and track records sketched from misleading data, leaders are often selected based on ideology, dysfunctional networks, and susceptibility to peer pressure, instead of independent thought, good decision making, and wisdom.

Given the evidence, a rational and intelligent path would be to reconstruct our thinking and behavior surrounding the entire topic of leadership and organizational structures, and then tailor that thinking specifically for the environment we actually face, with tools specifically designed for the actual task. For many cultures, such a path begins by emerging from deep denial and embracing evidence-based decision making. Once emerged from the pit of denial, they soon discover among other truths that resources are not infinite after all, personal accountability is not limited to the inefficiencies of organizations, and that both the problems and solutions we face are inextricable from computing, organizational management, and personal accountability. Only then will the path to sustainability began to take shape in the vision field in sufficient form to differentiate the forest from the trees.

Yet another of the many disciplines related to this topic defines psychosis as a “mental disorder characterized by symptoms, such as delusions that indicate impaired contact with reality”. An appropriate translation of insanity might be “refusal to adopt tools and models designed to achieve sustainability”, aka survival.

If this sounds familiar in your organization, it could well be traced to your leadership development model and process, for leaders are the decision makers who have budget authority. Perhaps it’s time for your organization to redefine strategic from clever to wise, and synchronize the organizational clock with present-day reality?

Above is a screen capture of an internal Kyield document that displays an illustration of the high costs of data silos to individual organizations, regions, and society based on actual cases we have studied; in some case based on public information and in others private, confidential information. This is intended for a slide-show type of presentation so does not go into great detail. Suffice to say that human suffering, lives lost–human and otherwise, and wars that could have been prevented that were not are inseparably intertwined with economics and ecology, which is why I have viewed this issue for 15 years as one ultimately of sustainability, particularly when considering the obstacles of silos to scientific discovery, innovation, and learning as well as crisis prevention.

As I was reading articles about Watson winning Jeopardy, I was thinking about one of my wife’s favorite TV shows; House. The main character, Dr. Gregory House–played brilliantly by Hugh Laurie, is an emotionally unstable genius who leads a team of physicians in a diagnostic unit at a fictional teaching hospital in Princeton, New Jersey.

In most episodes of House, the diagnosticians torture this viewer, the patient, and the patient’s family, with a game much like Jeopardy when each of the experts draw on their memory over days and weeks to match symptoms with disease and therapy. As if this isn’t painful enough for someone who has been focused on applying semantic technologies to improve healthcare efficiencies, the team invariably nearly kills the patient multiple times during the diagnostic Q & A before House has an epiphany when his pain-killer addicted mind finally connects the dots between Jungian philosophy, tropical parasites, chemical toxicity, and/or genetic disorders to miraculously save the patient (usually).

I have actually found myself talking over the TV at times (very rare otherwise), suggesting some version of “put those symptoms in a database”, or “structure your data and stop torturing your patients and viewers”, and “save a million dollars per patient in unnecessary healthcare costs”.

Of course this frustration comes only after a half-century of witnessing our education system—and by extension our society, favor memorization techniques rather than the more essential understanding of how to improve, innovate and solve problems. Most of our challenges as individuals, organizations, a planet, and species depend not only on the quality of data far beyond the abilities of any single human expert–or even group–that would be best categorized as hubris, but rather the integrity of the collective knowledge, and more importantly what we do with it, aka the decision process.

While a sharp memory is essential in solving real-world problems such as diagnostics in highly complex environments, memory is a task that is much better performed by computer networks than humans, particularly with properly structured data sourced and updated from human experts. Unfortunately, the vast majority of institutions can no longer afford Watson, House, or our education system, which is among the best arguments for the semantic web.

In the real-world, Dr. Gregory House and his team would be employing Watson for diagnostics to free up time for improved therapy, research, and caring for additional patients.

Observing lives lost and trauma from preventable tragedies is among the most frustrating experiences of my career. However, whatever frustration we feel pales in comparison to the pain victims and their family members experience. Prevention of human-caused catastrophes has long been a top priority of our R&D. We have a desire and an obligation to […]

It is truly an honor to share our recent announcement and welcome Vice Admiral Phil Wisecup USN (Ret.) to our board of directors. Phil joins Dr. Robert Neilson who is now special advisor to the board. As their bios only partially reflect, Phil and Rob are exceptional additions to Kyield’s leadership. Vice-Admiral James P. “Phil” Wisecup (Ret.) brings 40 […] […]

From theorem to market through multiple valleys of death and beyond This is a personal story about our real-world experience, which contains little resemblance to most of what is written about entrepreneurism and technology commercialization. While our journey has been longer than most, scientific commercialization (aka deep tech) typically requires two de […]

Even though some companies may seem well positioned, the fundamental economic and business environment is rapidly changing. To the best of my awareness, survival from this point forward will essentially require a strong AI OS for the super majority of organizations.

I wanted to share a general pattern that is negatively impacting organizations in part due to the compounding effect it has on the broader economy. Essentially this can be reduced to misapplying the company’s playbook in dealing with advanced technology (AI systems).

Every year, natural catastrophes (nat cat) are highly visible events that cause major damage across the world. In 2016 the cost of nat cats were estimated to be $175 billion, $50 billion of which were covered by insurance, reflecting severe financial losses for impacted areas.[i] The total cost of natural catastrophes since 2000 was approximately […]

The focus should be maximize benefits from our inventions, engineered systems and technologies to recreate a sustainable competitive advantage. One benefit of lagging behind other countries in infrastructure is that much progress has been made in recent years. Future projects can be embedded with hardware that enable intelligent networks, which can then be m […]

Learn about the background of Kyield and the multi-disciplinary science involved with AI systems, with a particular focus on AI augmentation for knowledge work and how to achieve a continuously adaptive learning organization (CALO).

The photo above represents a learning opportunity especially relating to survival and adaptation. Recently completed by my wife Betsy[i], the artwork was inspired by our visit to the Acoma Pueblo a few months ago, which is one of the oldest continuously inhabited communities in North America. Ancestors of current residents have lived on top of a 360-foot tal […]